import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
				from transformers.models.mobilevitva.modeling_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    make_divisible,
				)


if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def   _snake_case       (       self     :     int		)      ->	Any:
				_lowerCamelCase				   =  self.config_class(**self.inputs_dict		)
				self.parent.assertTrue(hasattr(snake_case__     ,  'width_multiplier'		)		)





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     Any     ,  snake_case__     :     Dict     ,  snake_case__     :     str=1_3     ,  snake_case__     :     List[str]=6_4     ,  snake_case__     :     Tuple=2     ,  snake_case__     :     int=3     ,  snake_case__     :     Optional[int]="swish"     ,  snake_case__     :     Optional[int]=3     ,  snake_case__     :     Dict=3_2     ,  snake_case__     :     List[str]=0.1     ,  snake_case__     :     Any=0.02     ,  snake_case__     :     List[Any]=True     ,  snake_case__     :     List[str]=True     ,  snake_case__     :     Tuple=1_0     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     Optional[int]=0.25     ,  snake_case__     :     Optional[int]=0.0     ,  snake_case__     :     Union[str, Any]=0.0     ,  )      ->	Any:
				_lowerCamelCase				   =  parent
				_lowerCamelCase				   =  batch_size
				_lowerCamelCase				   =  image_size
				_lowerCamelCase				   =  patch_size
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  make_divisible(5_1_2 * width_multiplier     ,  divisor=8		)
				_lowerCamelCase				   =  hidden_act
				_lowerCamelCase				   =  conv_kernel_size
				_lowerCamelCase				   =  output_stride
				_lowerCamelCase				   =  classifier_dropout_prob
				_lowerCamelCase				   =  use_labels
				_lowerCamelCase				   =  is_training
				_lowerCamelCase				   =  num_labels
				_lowerCamelCase				   =  initializer_range
				_lowerCamelCase				   =  scope
				_lowerCamelCase				   =  width_multiplier
				_lowerCamelCase				   =  ffn_dropout
				_lowerCamelCase				   =  attn_dropout
		def   _snake_case       (       self     :     Any		)      ->	Tuple:
				_lowerCamelCase				   =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				_lowerCamelCase				   =  None
				_lowerCamelCase				   =  None
				if self.use_labels:
						_lowerCamelCase				   =  ids_tensor([self.batch_size]     ,  self.num_labels		)
						_lowerCamelCase				   =  ids_tensor([self.batch_size, self.image_size, self.image_size]     ,  self.num_labels		)

				_lowerCamelCase				   =  self.get_config()

				return config, pixel_values, labels, pixel_labels
		def   _snake_case       (       self     :     List[Any]		)      ->	int:
				return MobileViTVaConfig(
				    image_size=self.image_size     ,  patch_size=self.patch_size     ,  num_channels=self.num_channels     ,  hidden_act=self.hidden_act     ,  conv_kernel_size=self.conv_kernel_size     ,  output_stride=self.output_stride     ,  classifier_dropout_prob=self.classifier_dropout_prob     ,  initializer_range=self.initializer_range     ,  width_multiplier=self.width_multiplier     ,  ffn_dropout=self.ffn_dropout_prob     ,  attn_dropout=self.attn_dropout_prob     ,  )
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Dict     ,  snake_case__     :     List[str]     ,  snake_case__     :     int     ,  snake_case__     :     Tuple		)      ->	Any:
				_lowerCamelCase				   =  MobileViTVaModel(config=snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(
				    result.last_hidden_state.shape     ,  (
				        self.batch_size,
				        self.last_hidden_size,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )     ,  )
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     Tuple     ,  snake_case__     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.num_labels
				_lowerCamelCase				   =  MobileViTVaForImageClassification(snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__     ,  labels=snake_case__		)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)		)
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     Tuple     ,  snake_case__     :     List[str]		)      ->	Optional[Any]:
				_lowerCamelCase				   =  self.num_labels
				_lowerCamelCase				   =  MobileViTVaForSemanticSegmentation(snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(
				    result.logits.shape     ,  (
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )     ,  )
				_lowerCamelCase				   =  model(snake_case__     ,  labels=snake_case__		)
				self.parent.assertEqual(
				    result.logits.shape     ,  (
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )     ,  )



		def   _snake_case       (       self     :     List[str]		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.prepare_config_and_inputs()
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  config_and_inputs
				_lowerCamelCase				   =  {'pixel_values': pixel_values}
				return config, inputs_dict





@require_torch
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   (
		    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)

		lowerCAmelCase_     =   (
		    {
		        'feature-extraction': MobileViTVaModel,
		        'image-classification': MobileViTVaForImageClassification,
		        'image-segmentation': MobileViTVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		def   _snake_case       (       self     :     List[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  MobileViTVaModelTester(self		)
				_lowerCamelCase				   =  MobileViTVaConfigTester(self     ,  config_class=snake_case__     ,  has_text_modality=snake_case__		)
		def   _snake_case       (       self     :     int		)      ->	Tuple:
				self.config_tester.run_common_tests()
		@unittest.skip(reason='MobileViTV2 does not use inputs_embeds'		)
		def   _snake_case       (       self     :     Optional[int]		)      ->	Dict:
				pass
		@unittest.skip(reason='MobileViTV2 does not support input and output embeddings'		)
		def   _snake_case       (       self     :     Tuple		)      ->	Dict:
				pass
		@unittest.skip(reason='MobileViTV2 does not output attentions'		)
		def   _snake_case       (       self     :     str		)      ->	int:
				pass
		@require_torch_multi_gpu
		@unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'		)
		def   _snake_case       (       self     :     Any		)      ->	str:
				pass
		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Optional[Any]:
				pass
		def   _snake_case       (       self     :     List[str]		)      ->	Union[str, Any]:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(snake_case__		)
						_lowerCamelCase				   =  inspect.signature(model.forward		)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_lowerCamelCase				   =  [*signature.parameters.keys()]

						_lowerCamelCase				   =  ['pixel_values']
						self.assertListEqual(arg_names[:1]     ,  snake_case__		)
		def   _snake_case       (       self     :     Optional[int]		)      ->	Dict:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	str:
				def check_hidden_states_output(snake_case__     :     Optional[int]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]		):
						_lowerCamelCase				   =  model_class(snake_case__		)
						model.to(snake_case__		)
						model.eval()

						with torch.no_grad():
								_lowerCamelCase				   =  model(**self._prepare_for_class(snake_case__     ,  snake_case__		)		)

						_lowerCamelCase				   =  outputs.hidden_states

						_lowerCamelCase				   =  5
						self.assertEqual(len(snake_case__		)     ,  snake_case__		)

						# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
						# with the width and height being successively divided by 2.
						_lowerCamelCase				   =  2
						for i in range(len(snake_case__		)		):
								self.assertListEqual(
								    list(hidden_states[i].shape[-2:]		)     ,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]     ,  )
								divisor *= 2

						self.assertEqual(self.model_tester.output_stride     ,  divisor // 2		)

				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  True
						check_hidden_states_output(snake_case__     ,  snake_case__     ,  snake_case__		)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						_lowerCamelCase				   =  True

						check_hidden_states_output(snake_case__     ,  snake_case__     ,  snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	List[Any]:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	List[str]:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__		)



		@slow
		def   _snake_case       (       self     :     Optional[int]		)      ->	str:
				for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowerCamelCase				   =  MobileViTVaModel.from_pretrained(snake_case__		)
						self.assertIsNotNone(snake_case__		)



def 	lowerCamelCase					(	)   ->    int:
		_lowerCamelCase				   =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
		return image





@require_torch
@require_vision
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		@cached_property
		def   _snake_case       (       self     :     List[Any]		)      ->	Optional[Any]:
				return (
				    MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'		)
				    if is_vision_available()
				    else None
				)
		@slow
		def   _snake_case       (       self     :     Optional[Any]		)      ->	List[Any]:
				_lowerCamelCase				   =  MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'		).to(
				    snake_case__		)

				_lowerCamelCase				   =  self.default_image_processor
				_lowerCamelCase				   =  prepare_img()
				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		).to(snake_case__		)

				# forward pass
				with torch.no_grad():
						_lowerCamelCase				   =  model(**snake_case__		)

				# verify the logits
				_lowerCamelCase				   =  torch.Size((1, 1_0_0_0)		)
				self.assertEqual(outputs.logits.shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]		).to(snake_case__		)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,  snake_case__     ,  atol=1e-4		)		)
		@slow
		def   _snake_case       (       self     :     Optional[Any]		)      ->	int:
				_lowerCamelCase				   =  MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'		)
				_lowerCamelCase				   =  model.to(snake_case__		)

				_lowerCamelCase				   =  MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'		)

				_lowerCamelCase				   =  prepare_img()
				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		).to(snake_case__		)

				# forward pass
				with torch.no_grad():
						_lowerCamelCase				   =  model(**snake_case__		)
				_lowerCamelCase				   =  outputs.logits

				# verify the logits
				_lowerCamelCase				   =  torch.Size((1, 2_1, 3_2, 3_2)		)
				self.assertEqual(logits.shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor(
				    [
				        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
				        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
				        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
				    ]     ,  device=snake_case__     ,  )

				self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,  snake_case__     ,  atol=1e-4		)		)



		@slow
		def   _snake_case       (       self     :     Optional[int]		)      ->	int:
				_lowerCamelCase				   =  MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'		)
				_lowerCamelCase				   =  model.to(snake_case__		)

				_lowerCamelCase				   =  MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'		)

				_lowerCamelCase				   =  prepare_img()
				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		).to(snake_case__		)

				# forward pass
				with torch.no_grad():
						_lowerCamelCase				   =  model(**snake_case__		)

				_lowerCamelCase				   =  outputs.logits.detach().cpu()

				_lowerCamelCase				   =  image_processor.post_process_semantic_segmentation(outputs=snake_case__     ,  target_sizes=[(5_0, 6_0)]		)
				_lowerCamelCase				   =  torch.Size((5_0, 6_0)		)
				self.assertEqual(segmentation[0].shape     ,  snake_case__		)

				_lowerCamelCase				   =  image_processor.post_process_semantic_segmentation(outputs=snake_case__		)
				_lowerCamelCase				   =  torch.Size((3_2, 3_2)		)
				self.assertEqual(segmentation[0].shape     ,  snake_case__		)
import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


A						=			{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Union[str, Any]							)   ->    Optional[Any]:
		return torch.atana(UpperCamelCase			,       UpperCamelCase							) / math.pi * 2


def 	lowerCamelCase					(	UpperCamelCase     :				str							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.sin(t * math.pi / 2							) ** 2
		_lowerCamelCase				   =  (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(UpperCamelCase			,       UpperCamelCase							)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		pass





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()

				_lowerCamelCase				   =  DiffusionAttnUnetaD(snake_case__     ,  n_attn_layers=4		)
				_lowerCamelCase				   =  deepcopy(self.diffusion		)
				_lowerCamelCase				   =  torch.quasirandom.SobolEngine(1     ,  scramble=snake_case__		)



def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    List[str]:
		_lowerCamelCase				   =  MODELS_MAP[model_name]['url']
		os.system(F"""wget {url} ./"""							)

		return F"""./{model_name}.ckpt"""


A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
A						=			{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
A						=			{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

A						=			{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

A						=			{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    int:
		if name.startswith('skip'							):
				return name.replace('skip'			,       RES_CONV_MAP['skip']							)

		# name has to be of format main.{digit}
		if not name.startswith('main.'							):
				raise ValueError(F"""ResConvBlock error with {name}"""							)

		return name.replace(name[:6]			,       RES_CONV_MAP[name[:6]]							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    Tuple:
		for key, value in ATTN_MAP.items():
				if name.startswith(UpperCamelCase							) and not isinstance(UpperCamelCase			,       UpperCamelCase							):
						return name.replace(UpperCamelCase			,       UpperCamelCase							)
				elif name.startswith(UpperCamelCase							):
						return [name.replace(UpperCamelCase			,       UpperCamelCase							) for v in value]
		raise ValueError(F"""Attn error with {name}"""							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				int=13							)   ->    Optional[int]:
		_lowerCamelCase				   =  input_string

		if string.split('.'							)[0] == "timestep_embed":
				return string.replace('timestep_embed'			,       'time_proj'							)

		_lowerCamelCase				   =  0
		if string.startswith('net.3.'							):
				depth += 1
				_lowerCamelCase				   =  string[6:]
		elif string.startswith('net.'							):
				_lowerCamelCase				   =  string[4:]

		while string.startswith('main.7.'							):
				depth += 1
				_lowerCamelCase				   =  string[7:]

		if string.startswith('main.'							):
				_lowerCamelCase				   =  string[5:]

		# mid block
		if string[:2].isdigit():
				_lowerCamelCase				   =  string[:2]
				_lowerCamelCase				   =  string[2:]
		else:
				_lowerCamelCase				   =  string[0]
				_lowerCamelCase				   =  string[1:]

		if depth == max_depth:
				_lowerCamelCase				   =  MID_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  'mid_block'
		elif depth > 0 and int(UpperCamelCase							) < 7:
				_lowerCamelCase				   =  DOWN_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""down_blocks.{depth}"""
		elif depth > 0 and int(UpperCamelCase							) > 7:
				_lowerCamelCase				   =  UP_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - depth - 1}"""
		elif depth == 0:
				_lowerCamelCase				   =  DEPTH_0_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - 1}""" if int(UpperCamelCase							) > 3 else 'down_blocks.0'

		if not string_left.startswith('.'							):
				raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}."""							)

		_lowerCamelCase				   =  string_left[1:]

		if "resnets" in new_layer:
				_lowerCamelCase				   =  convert_resconv_naming(UpperCamelCase							)
		elif "attentions" in new_layer:
				_lowerCamelCase				   =  convert_attn_naming(UpperCamelCase							)
				_lowerCamelCase				   =  new_string_left

		if not isinstance(UpperCamelCase			,       UpperCamelCase							):
				_lowerCamelCase				   =  prefix + '.' + new_layer + '.' + string_left
		else:
				_lowerCamelCase				   =  [prefix + '.' + new_layer + '.' + s for s in string_left]
		return new_string


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    int:
		_lowerCamelCase				   =  {}
		for k, v in state_dict.items():
				if k.endswith('kernel'							):
						# up- and downsample layers, don't have trainable weights
						continue

				_lowerCamelCase				   =  rename(UpperCamelCase							)

				# check if we need to transform from Conv => Linear for attention
				if isinstance(UpperCamelCase			,       UpperCamelCase							):
						_lowerCamelCase				   =  transform_conv_attns(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
				else:
						_lowerCamelCase				   =  v

		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Dict							)   ->    Optional[Any]:
		if len(UpperCamelCase							) == 1:
				if len(v.shape							) == 3:
						# weight
						_lowerCamelCase				   =  v[:, :, 0]
				else:
						# bias
						_lowerCamelCase				   =  v
		else:
				# qkv matrices
				_lowerCamelCase				   =  v.shape[0]
				_lowerCamelCase				   =  trippled_shape // 3
				for i in range(3							):
						if len(v.shape							) == 3:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[Any]:
		_lowerCamelCase				   =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

		_lowerCamelCase				   =  args.model_path.split('/'							)[-1].split('.'							)[0]
		if not os.path.isfile(args.model_path							):
				assert (
				    model_name == args.model_path
				), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
				_lowerCamelCase				   =  download(UpperCamelCase							)

		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_rate']
		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_size']

		_lowerCamelCase				   =  Object()
		_lowerCamelCase				   =  sample_size
		_lowerCamelCase				   =  sample_rate
		_lowerCamelCase				   =  0

		_lowerCamelCase				   =  UNetaDModel(sample_size=UpperCamelCase			,       sample_rate=UpperCamelCase							)
		_lowerCamelCase				   =  diffusers_model.state_dict()

		_lowerCamelCase				   =  DiffusionUncond(UpperCamelCase							)
		orig_model.load_state_dict(torch.load(args.model_path			,       map_location=UpperCamelCase							)['state_dict']							)
		_lowerCamelCase				   =  orig_model.diffusion_ema.eval()
		_lowerCamelCase				   =  orig_model.state_dict()
		_lowerCamelCase				   =  rename_orig_weights(UpperCamelCase							)

		_lowerCamelCase				   =  set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
		_lowerCamelCase				   =  set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

		assert len(UpperCamelCase							) == 0, F"""Problem with {renamed_minus_diffusers}"""
		assert all(k.endswith('kernel'							) for k in list(UpperCamelCase							)							), F"""Problem with {diffusers_minus_renamed}"""

		for key, value in renamed_state_dict.items():
				assert (
				    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
				), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
				if key == "time_proj.weight":
						_lowerCamelCase				   =  value.squeeze()

				_lowerCamelCase				   =  value

		diffusers_model.load_state_dict(UpperCamelCase							)

		_lowerCamelCase				   =  1_00
		_lowerCamelCase				   =  33

		_lowerCamelCase				   =  IPNDMScheduler(num_train_timesteps=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(UpperCamelCase							)
		_lowerCamelCase				   =  torch.randn([1, 2, config.sample_size]			,       generator=UpperCamelCase							).to(UpperCamelCase							)

		_lowerCamelCase				   =  torch.linspace(1			,       0			,       steps + 1			,       device=UpperCamelCase							)[:-1]
		_lowerCamelCase				   =  get_crash_schedule(UpperCamelCase							)

		_lowerCamelCase				   =  DanceDiffusionPipeline(unet=UpperCamelCase			,       scheduler=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(33							)
		_lowerCamelCase				   =  pipe(num_inference_steps=UpperCamelCase			,       generator=UpperCamelCase							).audios

		_lowerCamelCase				   =  sampling.iplms_sample(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       {}							)
		_lowerCamelCase				   =  generated.clamp(-1			,       1							)

		_lowerCamelCase				   =  (generated - audio).abs().sum()
		_lowerCamelCase				   =  (generated - audio).abs().max()

		if args.save:
				pipe.save_pretrained(args.checkpoint_path							)

		print('Diff sum'			,       UpperCamelCase							)
		print('Diff max'			,       UpperCamelCase							)

		assert diff_max < 1e-3, F"""Diff max: {diff_max} is too much :-/"""

		print(F"""Conversion for {model_name} successful!"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument(
				    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
				)
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				A						=			parser.parse_args()

				main(args)